Four UC Berkeley researchers developed a program to help grade papers during their time working as teaching assistants – and now, they've added artificial intelligence to their app to help instructors speed up the grading process. The team launched the online grading app Gradescope two years ago and have accumulated 10 million answers to around 100,000 questions from a wide range of college courses – the app has already shortened the grading process by 50 percent due to its friendly interface and the ability for multiple teaching assistants to grade papers in parallel. Their new AI features addresses three challenges: identify question types, distinguishing between different written marks, and recognizing handwriting. AI helps turn grading into an automated, highly repeatable exercise by learning to identify and group answers, and thus treat them as batches. The addition of AI promises to slash grading times by as much as 90 percent, said Sergey Karayev, a Gradescope co-founder who finished his PhD in computer science in 2014.

Data feedback loops are critical for machine learning platforms according to research firm Wikibon. In his latest research Professional Alert, an elaboration on the role of digital business platforms in machine learning explored in his previous Alert, "Digital Business Platform for Machine Learning Apps", Wikibon Big Data & Analytics Analyst George Gilbert takes a close look at the data feedback loops that are central to machine learning. He finds that data wrangling and defining the variables or features that drive the model are critical to establishing successful analysis and should be entrusted to data scientists. The data that feeds machine analysis often comes from sources that aren't curated, and that means that preparing data requires human expertise. Defining the correct variables and data features with which to separate valuable data from noise is critical.

Last year, McKinsey launched a multi-year study to explore the potential impact of automation technologies on jobs, organizations and the future of work. "Can we look forward to vast improvements in productivity, freedom from boring work, and improved quality of life?," its initial article on the study asked, or "Should we fear threats to jobs, disruptions to organizations, and strains on the social fabric?" Most jobs involve a number of different tasks or activities. Some of these activities are more amenable to automation than others. But just because some of the activities have been automated, does not imply that the whole job has disappeared. To the contrary, automating parts of a job will often increase the productivity and quality of workers by complementing their skills with machines and computers, as well as by enabling them to focus on those aspects of the job that most need their attention.

Humanoid robots still have problems staying upright, especially in tricky situations, but it's evident that they're making some progress. IHMC has posted a video showing Boston Dynamics' Atlas robot balancing on one foot on the edge of a plywood board about 0.8 inches thick. The feat is a "lucky run," IHMC admits (it's rare that the robot stays poised for so long), but it's relatively effortless. The worst you see before the fall is shaking as IHMC's algorithm sometimes makes poor estimates of the robot's state. As brief as the balancing act is, it's telling.

Data Science sits at the core of any analytical exercise conducted on a Big Data or Internet of Things (IoT) environment. Data science involves a wide array of technologies, business, and machine learning algorithms. The purpose of data science is just not doing machine learning or statistical analysis but also to derive insights out of the data that a user with no statistics knowledge can understand. In a fast paced environment such as Big Data and IoT where the type of data might vary over the course of time, it becomes difficult to maintain and recreate the models each and every time. This gap calls up for an automated way to manage the Data Science algorithms in those environments.

Smart home dream has already become a reality for many average Americans. According to one survey, 28 percent of U.S. adults and 47 percent of millennials have smart home products in their home. Of those who already have home automation gadgets, 81 percent are likely to purchase a home with smart technology already installed. Now, with public interest gaining momentum, smart home technology is poised to go far beyond smart sensors and individual gadgets. The future of the connected home is quickly becoming all about seamless integration, convenience, sustainability, and automation that can interact with personal health and living preferences to improve our lives.

Pepper, the humanoid robot, is coming up with all sorts of ways to serve its human overlords, including in eldercare. The little rolling robot took to the stage at the Disrupt SF Hackathon today to show how it could help our aging parents, should they fall and not be able to get up. According to PepperHealth creators Ethan Fan and Angelo Hizon – both Battlefield NY 2015 veterans for fitness app VimoFit – PepperHealth will also detect a rapidly rising heart rate, check in on how you're feeling, follow up on your sleeping habits and remind you to take your meds -- the robot will even call up an Uber for those unable to use a smartphone. It works by detecting abnormal vitals through the Watch such as a rapid heart rate or a fall and then Pepper "wakes up" and comes over to check out what's going on. Should something serious happen, Pepper will alert a built-in emergency system and give medics the elder's location to get them immediate assistance.

During the christmas vacation of 2015, I got a motivational boost again and decided try out Kaggle. So I spent quite some time experimenting with various algorithms for their Homesite Quote Conversion, Otto Group Product Classification and Bike Sharing Demand contests. The main takeaway from this was the experience of iteratively improving the results by experimenting with the algorithms and the data. I learned to trust my logic when doing machine learning. If tweaking a parameter or engineering a new feature seems like a good idea logically, it's quite likely that it actually will help.

Thus far, the dangers associated with self-driving cars have been largely relegated to their ability to safely navigate our roads and highways. But now the U.S. Department of Justice is interested in taking a closer look at a different kind of danger altogether -- and this has nothing to do with how well an autonomous vehicle can brake or steer. Rather, a new threat analysis team has been tasked with determining the potential security issues associated with not only self-driving cars, but other Internet of Things and connected devices as well. After all, if you can control everything from the palm of your hand, who's to say someone else can't do the same thing? Under Assistant Attorney General John Carlin, who heads the Justice Department's national security team, the new team seeks to secure the internet of things and protect it from potential terrorist threats.

The 1980s at the MIT Computer Science and Artificial Intelligence Laboratory seemed to outsiders like a golden age, but inside, David Chapman could already see that winter was coming. As a member of the lab, Chapman was the first researcher to apply the mathematics of computational complexity theory to robot planning and to show mathematically that there could be no feasible, general method of enabling AIs to plan for all contingencies. He concluded that while human-level AI might be possible in principle, none of the available approaches had much hope of achieving it. In 1990, Chapman wrote a widely circulated research proposal suggesting that researchers take a fresh approach and attempt a different kind of challenge: teaching a robot how to dance. Dancing, wrote Chapman, was an important model because "there's no goal to be achieved. You can't win or lose. It's not a problem to be solved…. Dancing is paradigmatically a process of interaction."